Does analog transmission achieve OPTA in an asymmetric Gaussian sensor network ?
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Bibliographic record
Abstract
We consider the estimation of a Gaussian source by a Gaussian sensor network where L distributed sensors transmit noisy observations of the source through a Gaussian multiple access channel to a fusion center. In a recent work, Gastpar showed that for a symmetric sensor network with no fading, analog (uncoded) transmission achieves the optimal performance theoretically attainable (OPTA). In this work, by comparing lower and upper bounds on the OPTA, we provide optimality conditions for analog transmission in an asymmetric Gaussian sensor network with deterministic fading. We also obtain an optimal power allocation scheme to minimize the mean-squared error distortion given a linear combination of powers (LCP) constraint. We determine optimality conditions for analog transmission under an LCP constraint, which includes the sum-power constraint as a special case.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it